one noise variable, logistic regression
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## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 39"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8068 -1.0493 0.5770 0.9415 2.5190
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18447 0.05074 3.635 0.000277 ***
## n1 2.20269 0.13545 16.262 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2256.7 on 1998 degrees of freedom
## AIC: 2260.7
##
## Number of Fisher Scoring iterations: 6
##
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"
## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"
## [1] "effects model, sigma= 39"
## [1] "one noise variable, logistic regression effects model, sigma= 39 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.213 -1.203 1.142 1.152 1.328
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06311 0.04938 1.278 0.20121
## n1 0.03592 0.01229 2.922 0.00347 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2764.0 on 1998 degrees of freedom
## AIC: 2768
##
## Number of Fisher Scoring iterations: 3
##
## [1] "one noise variable, logistic regression Laplace noised 39 train mean deviance 1.99379376005558"
## [1] "one noise variable, logistic regression Laplace noised 39 test mean deviance 2.00462219580913"
## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3619 -1.1570 0.9662 1.1980 1.2169
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.04838 0.04731 -1.023 0.30650
## n1 -0.06366 0.01954 -3.258 0.00112 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2761.8 on 1998 degrees of freedom
## AIC: 2765.8
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"
## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"
## [1] "********"
## [1] "one noise variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.000 2.000 2.000 2.001 2.003
## [1] 0.0006789706
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.001 2.002 2.003 2.003 2.017
## [1] 0.004185064
## [1] "********"
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## [1] "one noise variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.698 3.949 4.025 4.051 4.104 4.494
## [1] 0.2062731
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.000 2.000 2.002 2.002 2.011
## [1] 0.00283558
## [1] "********"
## [1] "*************************************************************"
one variable, logistic regression
## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 0"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1243 -1.1809 0.4704 1.1554 1.5778
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4731 0.0542 8.73 <2e-16 ***
## x1 3.1777 0.2114 15.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2434.7 on 1998 degrees of freedom
## AIC: 2438.7
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"
## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"
## [1] "effects model, sigma= 0"
## [1] "one variable, logistic regression effects model, sigma= 0 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1243 -1.1809 0.4704 1.1554 1.5778
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4731 0.0542 8.73 <2e-16 ***
## x1 3.1777 0.2114 15.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2434.7 on 1998 degrees of freedom
## AIC: 2438.7
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression Laplace noised 0 train mean deviance 1.75629049009229"
## [1] "one variable, logistic regression Laplace noised 0 test mean deviance 1.74484448505444"
## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0811 -1.1892 0.4966 1.1600 1.5642
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.45308 0.05326 8.508 <2e-16 ***
## x1 2.99703 0.20478 14.636 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2460.2 on 1998 degrees of freedom
## AIC: 2464.2
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"
## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"
## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.754 1.763 1.769 1.771 1.776 1.794
## [1] 0.01072632
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.755 1.763 1.770 1.771 1.775 1.792
## [1] 0.01027413
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## [1] "********"
## [1] "one variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.754 1.763 1.769 1.771 1.776 1.793
## [1] 0.01073833
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.754 1.762 1.771 1.771 1.777 1.793
## [1] 0.01184963
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, logistic regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 6"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5658 -0.9120 0.3055 0.8035 2.7112
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.68760 0.06161 11.16 <2e-16 ***
## x1 3.18452 0.23641 13.47 <2e-16 ***
## n1 2.45247 0.15572 15.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 1990.5 on 1997 degrees of freedom
## AIC: 1996.5
##
## Number of Fisher Scoring iterations: 6
##
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"
## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"
## [1] "effects model, sigma= 6"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 6 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0433 -1.1982 0.5191 1.0880 1.7140
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.49382 0.05753 8.583 <2e-16 ***
## x1 3.07418 0.20523 14.979 <2e-16 ***
## n1 0.02769 0.01552 1.784 0.0743 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2449.3 on 1997 degrees of freedom
## AIC: 2455.3
##
## Number of Fisher Scoring iterations: 3
##
## [1] "one variable plus noise variable, logistic regression Laplace noised 6 train mean deviance 1.76678082468646"
## [1] "one variable plus noise variable, logistic regression Laplace noised 6 test mean deviance 1.78498006179382"
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2012 -1.1757 0.5026 1.1657 1.5936
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.42346 0.05493 7.710 1.26e-14 ***
## x1 3.00699 0.20534 14.644 < 2e-16 ***
## n1 -0.05278 0.02435 -2.167 0.0302 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2455.4 on 1997 degrees of freedom
## AIC: 2461.4
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"
## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.732 1.762 1.768 1.769 1.776 1.792
## [1] 0.01311197
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.731 1.763 1.767 1.768 1.779 1.787
## [1] 0.01346241
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.315 3.456 3.589 3.610 3.751 4.039
## [1] 0.2108874
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.759 1.771 1.793 1.796 1.809 1.876
## [1] 0.03186603
## [1] "********"
## [1] "*************************************************************"